The DOI matches its Crossref record; 71 of the 71 references it cites were found in a registry or an index.
A Survey of Actor-Critic Reinforcement Learning: Standard and Natural Policy Gradients
Abstract
Policy-gradient-based actor-critic algorithms are amongst the most popular algorithms in the reinforcement learning framework. Their advantage of being able to search for optimal policies using low-variance gradient estimates has made them useful in several real-life applications, such as robotics, power control, and finance. Although general surveys on reinforcement learning techniques already exist, no survey is specifically dedicated to actor-critic algorithms in particular. This paper, therefore, describes the state of the art of actor-critic algorithms, with a focus on methods that can work in an online setting and use function approximation in order to deal with continuous state and action spaces. After starting with a discussion on the concepts of reinforcement learning and the origins of actor-critic algorithms, this paper describes the workings of the natural gradient, which has made its way into many actor-critic algorithms over the past few years. A review of several standard and natural actor-critic algorithms is given, and the paper concludes with an overview of application areas and a discussion on open issues.
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Cites
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Liu, D., Xue, S., Zhao, B., Luo, B., & Wei, Q. (2021) · IEEE Transactions on Systems, Man, and Cybernetics: Systems · doi:10.1109/tsmc.2020.3042876 · Cited by 708
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues
Sun, Y., Peng, M., Zhou, Y., Huang, Y., & Mao, S. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2924243 · Cited by 678
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Fu, Y., Lei, Y., Wang, T., Curran, W. J., Liu, T., & Yang, X. (2020) · Physics in Medicine & Biology · doi:10.1088/1361-6560/ab843e · Cited by 661
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Metadata
- Authors
- Grondman, I., Busoniu, L., Lopes, G. A. D., & Babuska, R.
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)
- Published
- 2012-11
- Volume
- 42
- Issue
- 6
- Pages
- 1291-1307
- ISSN
- 1094-6977 (ISSN-L)
- 1558-2442
- OpenAlex
- W2046376809
- Cited by
- 1,042
- References
- 71
- FWCI
- 14.57
- Type
- journal article
- Language
- en
- Open access
- closed
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